df_sec = update_securities_df(Securities_file_import_from_csv, update = False)
data_type='Adj Close'
df = df_sec
df = df.loc[(df['Type'] == 'sector') | (df['Type'] == 'inx')]
df = df['Ticker'].values.tolist()
sector_price_data = get_prices(df, data_type = 'Adj Close', start_date = start_date,
force_update = True, end_date = None,
account = SECURITY_ARRAY[0],
FOLDER_LOCATION = META_FILE_FOLDER_LOCATION)
inx_price_data = sector_price_data[[INX]].copy()
#inx_price_data = inx_price_data.rename(columns={inx_price_data[0]: INX })
inx_price_data['MA50_index']=inx_price_data[INX].rolling(50).mean()
inx_price_data['MA200_index']=inx_price_data[INX].rolling(200).mean()
#inx_price_data.index = pd.to_datetime(inx_price_data.index)
# Function calls
Market_indicator_signal,mkt_ind = get_mkt_indicator(inx_price_data)
Average_return = _1_3_6_9_12_month_returns(sector_price_data)
FRED_data_mort = get_FRED_data('MORTGAGE30US','mort',start_date,0,1,FRED_API)
FRED_data_LEI = get_FRED_data('USSLIND','LEI',start_date,0,1,FRED_API)
FRED_data_inf = get_FRED_data('CPILFESL','inf',start_date,12,100,FRED_API)
FRED_data_10_2 = get_FRED_data('T10Y2Y','10_2',start_date,0,1,FRED_API)
FRED_data_RF = get_FRED_data('DTB3','RF',start_date,0,1,FRED_API)
FRED_data_ICSA = get_FRED_data('ICSA','UnEmp',start_date,52,1,FRED_API)
combined_12_mo_return = pd.concat([FRED_data_RF, FRED_data_inf,
FRED_data_LEI, FRED_data_mort,
FRED_data_10_2, FRED_data_ICSA],
axis=1, sort=False)
combined_12_mo_return = combined_12_mo_return.fillna(method='ffill')
combined_12_mo_return = combined_12_mo_return.fillna(method='bfill')
temp = bollinger_bands_graph(inx_price_data,INX);
# Display
display = display_relative_strength(Average_return,
Market_indicator_signal,
mkt_ind,
securities_file_location)
# S&P CHART
font = 1.0
inx_price_data = inx_price_data.reset_index()
Upper_bollinger = go.Scatter(x=inx_price_data['Date'],
y=inx_price_data['Upper Band'],
fill = "tonexty",
line=go.scatter.Line(color='gray', width = font*.0001),
opacity=0.8,
name='Bollinger',
text='Bollinger')
Lower_bollinger = go.Scatter(x=inx_price_data['Date'],
y=inx_price_data['Lower Band'],
fill = "tonexty",
line=go.scatter.Line(color='gray', width = font*.0001),
opacity=0.8,
name='Bollinger',
text='Bollinger')
price_data = go.Scatter(x=inx_price_data['Date'],
y=inx_price_data['spy'],
line=go.scatter.Line(color='black', width = font*3),
opacity=0.8,
name='spy',
text='price')
shrt_MA_data = go.Scatter(x=inx_price_data['Date'],
y=inx_price_data['MA50_index'],
line=go.scatter.Line(color='blue', width = font*2),
opacity=0.8,
name='MA50',
text='MA50')
long_MA_data = go.Scatter(x=inx_price_data['Date'],
y=inx_price_data['MA200_index'],
line=go.scatter.Line(color='red', width = font*2),
opacity=0.8,
name='MA200',
text='MA200')
layout = go.Layout(height=800, width=1400, font=dict(size=20),
title='S&P Market chart',
yaxis=dict(title='Prices', showspikes=True,
fixedrange = False),
xaxis=dict(title='Date',
rangeslider=dict(visible=True), showspikes=True,
rangeselector=dict(
buttons=list([
dict(count=1, label="1m", step="month", stepmode="backward"),
dict(count=6, label="6m", step="month", stepmode="backward"),
dict(count=1, label="YTD", step="year", stepmode="todate"),
dict(count=1, label="1y", step="year", stepmode="backward"),
dict(count=6, label="5y", step="year", stepmode="backward"),
dict(step="all")
]))))
fig = go.Figure(data=[shrt_MA_data, long_MA_data, price_data, Upper_bollinger, Lower_bollinger], layout=layout)
iplot(fig)
# Econometric graphs
econ_data = combined_12_mo_return.reset_index()
econ = plotly_time_series(econ_data_labels,econ_data_titles,econ_data)
econ2 = plotly_time_series(econ_data_labels2,econ_data_titles2,econ_data)